Advanced data science systems and methods useful for auction pricing optimization over network

ABSTRACT

An advanced data platform may receive an asset pricing request containing information about an asset. An optimization engine may determine a predicted price for the asset at different locations and times and compute a price matrix accordingly. The engine may identify an optimized predicted price from the price matrix, taking into account the spatial and temporal factors and various optimization conditions. A view for presentation of the optimized predicted price for the asset on a client device is generated and communicated to the client device over a network. When the asset is a vehicle, the engine may compute a linear regression model that defines a set of input variables with associated regression coefficients, the set of input variables comprising input variables representing attributes describing the vehicle.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a conversion of, and claims a benefit of priorityunder 35 U.S.C. §119(e) from U.S. Provisional Application No.62/197,256, filed Jul. 27, 2015, entitled “AUCTION PRICE OPTIMIZATIONSYSTEM AND METHOD,” which is fully incorporated by reference herein inits entirety, including the appendix.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to facsimile reproduction of the patent document or the patentdisclosure as it appears in the Patent and Trademark Office patent fileor records, but otherwise reserves all copyright rights thereto.

TECHNICAL FIELD

This disclosure relates generally to advanced data science systems andmethods of processing transaction data on data platforms operating in anetworked computing environment. More particularly, this disclosurerelates to advanced data processing systems and methods for assetpricing optimization in a networked computing environment. Still moreparticularly, various representative embodiments disclosed herein relateto systems, methods and tools for analyzing and evaluating automotivedata to predict and optimize automobile pricing. Even more particularly,various representative embodiments provide a new optimization engineconfigured for determining a maximum selling price of a subject vehicleat auction or wholesale relative to time and location, given a specifictrim, options and vehicle history information associated with thesubject vehicle.

BACKGROUND OF THE RELATED ART

Many challenges exist in auction pricing, particularly in used vehicleauction pricing. For example, one challenge is to accurately estimatethe price a vehicle will command at auction given the subject vehicle'strim, options, condition, mileage and other factors that couldpotentially affect valuation of a subject vehicle. Due at least to thevariability of data scarcity, size, and quality among disparate datasources and the computational power, memory, and data storage required,processing raw vehicle data can be cumbersome, time consuming, andprohibitively expensive. Moreover, while conventional auction pricingsystems may provide simplified valuation mechanisms, they do notoptimize auction valuations taking spatial (e.g., geographic location)and temporal (e.g., time-of-day, time-of-week, time-of-month,season-of-year) factors into account.

A conventional method qualitatively estimates wholesale values of usedcars at a “global” or fixed level of vehicle grouping, independent of,e.g., location with respect to where the vehicle will be auctioned;e.g., all 2012 Ford Mustang GTs would be considered to have similarvalues. This method has several drawbacks. For example, the identifiedvalue may be inaccurate as a result of sparse data, especially for oldervehicles. Additionally, if the location of the auction is ignored, fixedvalue estimates can be significantly inaccurate owing to substantialdifferences in demand for a given geographic market. For instance, thevalue of a 2014 Ford Mustang GT convertible being auctioned in Key West,Fla. may be significantly higher than the value it would command atauction in Estcourt Station, Me.

Another conventional method qualitatively estimates wholesale values forused cars at a fixed level of vehicle grouping, independent of, e.g.,time-of-year. For instance, the value of a 2014 Ford Mustang GTconvertible being auctioned in the month of June may be substantiallyhigher than the value it would command at auction in January.

The inability to accurately determine the value of an asset, such as aused vehicle, can make it difficult for an asset owner, manager, ordealer such as an automobile dealer to buy and sell in a way that theycan, with a degree of certainty, get a return on the maximum value ofthe asset. Consider an automobile dealer who must sell a given portionof used vehicle inventory at auction on, e.g., a quarterly basis tomeet, e.g., lot turnover and year, make, model, trim distributionobjectives. On one hand, the amount of profit realized by the automobiledealer will affect the decision to auction a particular vehicle or totry selling the vehicle at retail. On the other hand, maintaining aparticular vehicle in inventory will have attendant costs relating to,e.g., transportation, repair, residence time on the dealer's lot,opportunity costs associated with another vehicle that might afford thedealer a larger profit, and/or the like.

The aforementioned predicament faced by an automobile dealer is only oneexample of what an asset owner, manager, or dealer may face, forinstance, when a portfolio of assets or a portion thereof is up forauction or wholesale. When the price that each asset is likely tocommand at auction or for wholesale cannot be accurately determined, theindividual pricing inaccuracies can add up to a significantly loss inprofit. Furthermore, when an asset owner, manager, or dealer cannotaccurately determine where and when an asset should be commissioned forauction or offer for sale to obtain the best price, the missedopportunities can significantly affect their bottom line. Consequently,there is room for innovations and improvements.

SUMMARY OF THE DISCLOSURE

Disclosed embodiments provide predictive pricing tools and pricing datathrough, e.g., an auction price optimization interface and informationdisplay presented to dealers. Data obtained thereby may assist dealerswith the administration of inventory management, operation forecastingand commissioning of vehicles for sale at auction in order to realizethe best price the vehicles are likely to command.

Disclosed predictive pricing tools analyze historical auctiontransaction data to quantitatively and intelligently predict prices atwhich various transactions are likely to occur at auction. Historicaldata can be leveraged to construct parameters for analysis in accordancewith a representatively disclosed optimization model. Subject vehicleinformation and current supply/demand data (e.g., inventory and auctionstatistics) may be used to prepare a probability distribution of priceslikely to be obtained at auction for a subject automobile. The resultingprobability distribution may be converted to, or otherwise expressed in,an optimal auction price, taking into account factors such as spatialand temporal factors that were not considered in existing auction pricevaluation systems.

Representative auction pricing tools disclosed herein allow networkclients (e.g., networked devices associated with dealers, originalequipment manufacturers, auto finance captive companies, etc.) topredict auction values for vehicle assets via network access to a systemthat performs quantitative numerical analysis. Representative systemsdisclosed herein not only provide auction-based valuation for aparticular VI N, trim, style, etc., but also provide auction priceoptimization functionality configured for optimizing profits and/orreducing costs associated with vehicle assets.

Representative embodiments disclosed herein provide advantages overconventional approaches for predicting the price a subject vehicle willcommand at auction. Inasmuch as there is not an auction value estimationformulation in the conventional art that incorporates spatial andtemporal factors for optimizing the price a subject vehicle is likely tocommand at auction, significant advantages for automobile dealers (andother entities) may be achieved in accordance with variousrepresentative embodiments; e.g., straightforward identification of whenand where to commission a vehicle for sale at auction. As compared withconventional approaches based on qualitative (subjective) pastexperiences that provide less accurate guidance on how to optimizevalues of their vehicle assets, representatively disclosed embodimentsprovide network clients with advanced data analysis tools for accuratelypricing vehicle assets and quantitatively (objectively) optimizingprofits and/or reducing costs associated with vehicle assets in a fast,efficient, consistent, reliable, and reproducible manner.

In some embodiments, a method for auction pricing optimization over anetwork may include a data platform receiving, from a client devicecommunicatively connected thereto over the network, an asset pricingrequest containing information about an asset. The data platform mayoperate on at least one server machine and support a network site. Anoptimization engine running on the data platform may operate todetermine a predicted price for the asset at each of a plurality oflocations at each of a plurality of times. The optimization engine mayoperate to utilize the predicted prices thus determined and compute aprice matrix containing a plurality of values for the predicted price,each value of the plurality of values associated with a specificlocation of the plurality of locations at a specific time of theplurality of times. The optimization engine may operate to identify,from among the plurality of values at the plurality of locationsrelative to the plurality of times in the price matrix, an optimizedpredicted price for the asset. This can be done by taking into accountthe spatial and temporal factors that may affect the predicted price atdifferent locations and at different times in view of variousoptimization conditions. For example, the optimization engine maycompare values associated with different times of the plurality of timesrelative to a given location of the plurality of locations; may comparevalues associated with different locations of the plurality of locationsrelative to a given time of the plurality of times; and may comparevalues associated with different locations of the plurality of locationsat different times of the plurality of times relative to a givenlocation of the plurality of locations at a given time of the pluralityof times. A view for presentation of the optimized predicted price forthe asset on the client device can be generated and communicated to theclient device over the network.

In some embodiments, in determining the predicted price for the asset ateach of the plurality of locations at each of the plurality of times,the optimization engine may operate to determine a predicted price forthe asset at a given location at each of the plurality of times and alsodetermine a predicted price for the asset at different locations of theplurality of locations other than the given location at each of theplurality of times. That is, the predicted price for the asset can bedetermined with respect to a given location for each of the plurality oftimes and with respect to a given time for each of the plurality oflocations other than the given location.

In some embodiments, in determining the predicted price for the asset ateach of the plurality of locations at each of the plurality of times,the optimization engine may operate to compute a regression model (inthe form of an equation) that defines a set of input variables withassociated regression coefficients, the set of input variablescomprising a first input variable representing supply of the asset at agiven location of the plurality of locations, a second input variablerepresenting supply of the asset at different locations of the pluralityof locations other than the given location, and a third input variablerepresenting supply of competitive assets at the given location.

In some embodiments, the asset of interest can be a vehicle. Indetermining a predicted price for the vehicle at each of a plurality oflocations at each of a plurality of times, the optimization engine mayoperate to compute a regression model that defines a set of inputvariables with associated regression coefficients, the set of inputvariables comprising input variables representing attributes describingthe vehicle.

In some embodiments, the asset may comprise a plurality of assets. Anasset pricing request for pricing a plurality of assets may containinformation about each of the plurality of assets. In such cases, theoptimization engine may operate to determine an optimized predictedprice for each of the plurality of assets and generate a viewaccordingly.

In some embodiments, the asset may comprise a set of vehicles, in whichcase, information contained in an asset pricing request may includeinformation about each of the set of vehicles. The optimization enginemay operate to perform a valuation of the set of vehicles with respectto a plurality of locations and a plurality of times, treating the setof vehicles as a whole.

One embodiment comprises a system comprising at least one processor andat least one non-transitory computer-readable storage medium that storescomputer instructions translatable by the at least one processor toperform a method substantially as described herein. Another embodimentcomprises a computer program product having at least one non-transitorycomputer-readable storage medium that stores computer instructionstranslatable by at least one processor to perform a method substantiallyas described herein.

Numerous other embodiments are also possible. Skilled artisansappreciate that the representative embodiments of advanced data sciencesystems and methods disclosed herein can find utility beyond automotiveauction pricing systems. For example, suitable price prediction systemsmay implement the advanced data science systems and methods disclosedherein to efficiently and significantly optimize price predictions onvarious types of enterprise assets, commodities, and/or durable goods inaddition to automobiles.

These, and other, aspects of the disclosure will be better appreciatedand understood when considered in conjunction with the followingdescription and the accompanying drawings. It should be understood,however, that the following description, while indicating variousrepresentative embodiments of the disclosure and numerous specificdetails thereof, is given by way of illustration and not of limitation.Many substitutions, modifications, additions and/or rearrangements maybe made within the scope of the disclosure without departing from thespirit thereof, and the disclosure contemplates and includes all suchsubstitutions, modifications, additions and/or rearrangements.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings accompanying and forming part of this specification areincluded to representatively depict certain aspects of the disclosure.It should be noted that the features illustrated in the drawings are notnecessarily drawn to scale. A more complete understanding of thedisclosure and the advantages thereof may be acquired by referring tothe following description, taken in conjunction with the accompanyingdrawings in which like reference numbers indicate like features andwherein:

FIG. 1 depicts a representative example of a topology that includes adata platform operating in a networked computing environment wherevarious embodiments of the systems and methods disclosed herein can beimplemented;

FIG. 2 depicts a diagrammatic representation of an example pricingsystem that includes an optimization engine driven by advanced datascience methods disclosed herein according to some embodiments;

FIG. 3 depicts a diagrammatic representation of functional modules ofanother example pricing system according to some embodiments;

FIG. 4 depicts a diagrammatic representation of data analysis andcomputation components of an example optimization engine according tosome embodiments;

FIG. 5 depicts a flowchart illustration of a data-driven priceprediction and optimization method according to some embodiments; and

FIG. 6 depicts a diagrammatic representation of one example embodimentof a data processing system suitable for implementing embodimentsdisclosed herein.

DETAILED DESCRIPTION

The invention and various features and advantageous details thereof areexplained more fully with reference to the non-limiting embodiments thatare representatively illustrated in the accompanying drawings anddetailed in the following description. Descriptions of well-knownstarting materials, processing techniques, components and equipment areomitted so as not to obscure the invention in detail. It should beunderstood, however, that the detailed description and the specificexamples, while indicating various representative embodiments of theinvention, are given by way of illustration only and not by way oflimitation. Various substitutions, modifications, additions and/orrearrangements within the spirit and/or scope of the underlyinginventive concept will become apparent to those skilled in the art fromthis disclosure. Embodiments discussed herein may be implemented insuitable computer-executable instructions that may reside on a computerreadable medium (e.g., a hard disk (HD)), hardware circuitry and/or thelike, or any combination thereof.

FIG. 1 depicts a representative embodiment of a topology that may beused to implement various embodiments of the systems and methodsdisclosed herein. Topology 100 comprises a set of entities includingvehicle data system 120 (also referred to herein as the “TrueCar” systemor data platform) which operates on one or more server machines at thebackend (e.g., behind a firewall of a private network at the serverside) that supports a network site and that is coupled through network170 to computing devices 110 (e.g., computer systems, personal dataassistants, kiosks, dedicated terminals, mobile telephones, smartphones, etc.), and one or more computing devices at inventory companies140, original equipment manufacturers (OEM) 150, sales data companies160, financial institutions 182, external information sources 184,departments of motor vehicles (DMV) 180 and one or more associated pointof sale locations; e.g., in this embodiment, car dealers 130 a . . . 130n. Computing devices 110 may be used by consumers while conducting asearch for consumer goods and/or services, such as automobiles. Network170 may comprise, for example, a wireless or wired communication networksuch as the Internet or wide area network (WAN), publicly switchedtelephone network (PTSN) or any other type of electronic ornon-electronic communication link such as mail, courier service, and/orthe like.

Vehicle data system 120 may comprise one or more computer systems withcomputer processors executing instructions embodied on one or morenon-transitory computer-readable media where the instructions areconfigured for performing at least some of the functionality associatedwith various representative embodiments disclosed herein. Theseapplications may include a vehicle data application 190 comprising oneor more applications (e.g., instructions embodied on one or morenon-transitory computer-readable media) configured to implement aninterface module 192, data gathering module 194, and processing module196 utilized by the vehicle data system 120. Furthermore, vehicle datasystem 120 may include data store 122 operable to store obtained data124, data 126 determined during operation, models 128 that may comprisea set of dealer cost model or price ratio models, or any other type ofdata associated with various embodiments disclosed herein or determinedduring implementation of such embodiments.

Vehicle data system 120 may provide a wide degree of functionality,including utilizing one or more interfaces 192 configured to, forexample, receive and respond to queries from users at computing devices110; interface with inventory companies 140, manufacturers 150, salesdata companies 160, financial institutions 182, auction source(s) 186,DMVs 180 or dealers 130 a . . . 130 n to obtain data; or provide dataobtained, or determined, by vehicle data system 120 to any of inventorycompanies 140, manufacturers 150, sales data companies 160, financialinstitutions 182, DMVs 180, external data sources 184 or dealers 130 a .. . 130 n. It will be understood that a particular interface 192utilized in a given context may depend on the functionality beingimplemented by vehicle data system 120, the type of network 170 utilizedto communicate with any particular entity, the type of data to beobtained or presented, the time interval at which data is obtained fromthe entities, the types of systems utilized at the various entities,etc. Accordingly, these interfaces may include, for example, web pages,web services, a data entry or database application to which data can beentered or otherwise accessed by an operator, or almost any other typeof interface which may be desirable for use in a particular context.

In general, vehicle data system 120 may obtain data from a variety ofsources via operation of interface 192, including one or more inventorycompanies 140, manufacturers 150, sales data companies 160, financialinstitutions 182, DMVs 180, external data sources 184 or dealers 130 a .. . 130 n and store such data in data store 122. This data may then begrouped, analyzed or otherwise processed by vehicle data system 120 todetermine, e.g., desired data 126, or for implementation in models 128which may also be stored in data store 122.

A user at computing device 110 may access the vehicle data system 120through the provided interface(s) 192 and specify certain parameters,such as a desired vehicle configuration or incentive data the userwishes to apply, if any. Vehicle data system 120 can select a particularset of data in the data store 122 based on the user-specifiedparameters, process the data set using processing module 196 and models128, generate interface(s) using interface module 192 using the selecteddata set on computing devices 110 and data determined from theprocessing, and present interfaces to the user at the user's computingdevice 110. Interface(s) 192 may visually/graphically present theselected data set to the user in a highly intuitive and useful manner.

A visual interface may present at least a portion of the selected dataset as a price curve, bar chart, histogram, etc. that reflectsquantifiable prices or price ranges (e.g., “average,” “good,” “great,”“overpriced,” etc.) relative to reference pricing data points (e.g.,invoice price, MSRP, dealer cost, market average, internet average,etc.). Using these types of visual presentations may enable a user tobetter understand the pricing data related to a specific vehicleconfiguration. Additionally, by presenting data corresponding todifferent vehicle configurations in a substantially identical manner, auser can readily make comparisons between pricing data associated withdifferent vehicle configurations. To further aid the understanding for auser of the presented data, the interface may also present data relatedto incentives which were utilized to determine the presented data or howsuch incentives were applied to determine presented data.

Turning to various other elements of topology 100, dealer 130 a may be aretail outlet for consumer goods and/or services, such as vehiclesmanufactured by one or more of OEMs 150. Dealer 130 a may employ adealer management system (DMS) 132 a to track or otherwise manage sales,finance, parts, service, inventory, and back office administrationfunctions. Since many DMSs 132 a . . . 132 n are Active Server Pages(ASP) based, transaction data 134 a . . . 134 n (e.g., transaction data134 a) may be obtained directly from a DMS (e.g., DMS 132 a) with a“key” (e.g., an ID and password with set permissions within DMS 132 a)that enables data to be retrieved from the DMS (in this example, DMS 132a). Many dealers 130 a . . . 130 n may also have one or more web sitesthat may be accessed over network 170, where pricing data pertinent to adealer may be presented on those web sites, including any predetermined,or upfront pricing. This price is typically termed the “no haggle” price(i.e., price without negotiation), and may be deemed a “fair” price byvehicle data system 120. To the extent a DMS also tracks and/or managesauction data, vehicle data system 120 may obtain or receive auction datafrom the DMS to compute and optimize prices for wholesale/auctionmarket(s). Additionally or alternatively, vehicle data system 120 mayobtain or receive auction data from auction source(s) 186.

Inventory companies 140 may comprise one or more inventory pollingcompanies, inventory management companies or listing aggregators whichmay obtain and store inventory data from one or more of dealers 130 a .. . 130 n (for example, obtaining such data from DMSs 132 a . . . 132n). Inventory polling companies are typically commissioned by the dealerto pull data from DMSs 132 a . . . 132 n and format the data for use onweb sites and by other systems. Inventory management companies manuallyupload inventory information (e.g., photos, description, specifications)on behalf of the dealer. Listing aggregators get their data by“scraping” or “spidering” web sites that display inventory content andreceiving direct feeds from listing web sites (for example,AutoTrader.com, FordVehicles.com, etc.). In addition to retail inventorydata or listings, one or more inventory polling companies, inventorymanagement companies and/or listing aggregators may obtain and storeinventory data from auction sources such as auction houses. Inventorydata at auction provides an indication of a possible supply at a certainauction and can be used to determine the impact on price.

DMVs 180 may collectively include any type of government entity to whicha user provides data related to a vehicle. For example, when a userpurchases a vehicle, it must be registered with the state (for example,DMV, Secretary of State, etc.) for tax and titling purposes. This datatypically includes vehicle attributes (for example, year, make, model,mileage, etc.) and sales transaction prices for tax purposes.

Financial institution 182 may be any entity such as a bank, savings andloan, credit union, etc. that provides any type of financial services toa participant involved in the purchase of a vehicle. For example, when abuyer purchases a vehicle, they may utilize a loan from a financialinstitution, where the loan process usually requires two steps: applyingfor the loan and contracting the loan. These two steps may utilizevehicle and consumer information in order for the financial institutionto properly assess and understand the risk profile of the loan.Typically, both the loan application and loan agreement include proposedand actual sales prices of the vehicle.

Sales data companies 160 may include any entities that collect any typeof vehicle sales data. For example, syndicated sales data companiesaggregate new and used sales transaction data from DMSs 132 a . . . 132n of particular dealers 130 a . . . 130 n. These companies may haveformal agreements with dealers 130 a . . . 130 n that enable them toretrieve data from dealers 130 a . . . 130 n in order to syndicate thecollected data for the purposes of internal analysis or externalpurchase of the data by other data companies, dealers, and OEMs.

Manufacturers 150 may comprise entities that actually build the vehiclessold by dealers 130 a . . . 130 n. To guide the pricing of theirvehicles, manufacturers 150 may provide an Invoice price and aManufacturer's Suggested Retail Price (MSRP) for both vehicles andoptions for those vehicles to be used as general guidelines for thedealer's cost and price. These fixed prices are set by the manufacturerand may vary slightly by geographic region.

External information sources 184 may comprise any number of othervarious sources, online or otherwise, which may provide other types ofdesired data; for example, data associated with vehicles, pricing,demographics, economic conditions, markets, locale(s), consumers, etc.

It should be here noted that not all of the various entities depicted intopology 100 are necessary, or even desired, in representativeembodiments disclosed herein, and that certain functionality describedwith respect to the entities representatively depicted in topology 100may be combined into a single entity or eliminated altogether.Additionally, in some embodiments other data sources not shown intopology 100 may be utilized. Topology 100 is therefore presented forillustrative purposes only and should in no way be taken as imposing anylimitations on various embodiments disclosed herein.

FIG. 2 depicts a diagrammatic representation of example pricing system200 that may reside in topology 100 of FIG. 1 and that may beimplemented as part of vehicle data system 120 described above. In thisexample, system 200 includes pricing engine 210 and optimization engine240 driven by advanced data science methods disclosed herein accordingto some embodiments

As generally depicted in FIG. 2, system 200 may implement dataprocessing process 201 and advanced data processing process 205. Process201 may be configured for predicting the price an item of interest(which, as a non-limiting example, can be a subject vehicle) is expectedto command at auction.

More specifically, process 201 may entail providing input 212 containingvehicle information for a subject vehicle to pricing engine 210. Input212, or at least a portion thereof, may include information received bysystem 200 (e.g., an embodiment of vehicle data system 120 of FIG. 1)from a client device (e.g., client device 110) over a network (e.g.,network 170). Examples of vehicle information that may be included ininput 212 may include the vehicle identification number (VIN) for thesubject vehicle, a trim identifier associated with the VI N, astandardized or normalized identifier (e.g., a “Chrome style” identifierused by automotive web sites to obtain automotive data such as carstyles, as known to those skilled in the art, see e.g.,www.chromedata.com).

The client device may be associated with a user (e.g., a visitor of anetwork site supported by system 200, an employee of an auction housesuch as auction source 186, an authorized user of a dealer such asdealer 130 a, etc.). Information received from the client device mayinclude a request for auction pricing prediction for the subject vehicleand vehicle data for the subject vehicle. In response to the request,system 200 may prepare input 212 based on the vehicle data. Preparationof input 212 may include, for instance, determining what data is neededin addition to the information received from the client device,gathering additional data from a data store (e.g., data store 122)and/or disparate data sources (e.g., one or more data sourcescommunicatively coupled to vehicle data system 120 over network 170).Alternatively or additionally, preparation of input 212 may includetranslating, formatting, and/or converting the received data into aformat supported by pricing engine 210. The prepared input 212 is thencommunicated to pricing engine 210.

Alternatively or additionally, system 200 may determine auction pricingprediction for a set of assets, generate a valuation report orvisualization (e.g., a view for presentation on the client device via auser interface) listing the predicted prices for the set of assets, andcommunicate the valuation report or visualization to a client deviceover a network, perhaps on demand or automatically on a periodic basis.

Based on input 212, pricing engine 210 may operate to determine output214. Output 214 may contain a price valuation, representing a price thatthe subject vehicle is expected to command at auction or wholesale(e.g., trade or sale in bulk or large quantities) based on historicaltransaction data associated with a plurality of vehicles having the sameor similar vehicle characteristics as the subject vehicle (e.g., thesame or similar year, make, model, trim, mileage, and/or condition). Thehistorical transaction data may be obtained from disparate sources andstored in a data store accessible by pricing engine 210 (e.g., datastore 122 described above). Pricing engine 210 may include any suitableprice valuation tools capable of determining a price that a subjectvehicle is, to a certain degree of confidence, expected to command atauction or wholesale.

To augment pricing engine 210, and the output that it produces, system200 may further include advanced data processing process 205implementing a robust price and profit optimization mechanism. Asillustrated in FIG. 2, in some embodiments, process 205 may includeproviding the same input 212 to optimization engine 240 (also referredto herein as intelligence engine 240). In this case, informationreceived from the client device may include a request for auctionpricing optimization for a subject vehicle and vehicle data for thesubject vehicle. In response to the request, system 200 may prepareinput 212 for optimization engine 240 based on the vehicle data.Preparation of input 212 for optimization engine 240 may be the same orsimilar to preparation of input 212 for pricing engine 210 describedabove. The prepared input 212 is then communicated to optimizationengine 240. Based on input 212, optimization engine 240 may operate toevaluate spatial and temporal factors 250 to determine optimized pricevaluation 260, representing a quantitatively optimized price that thesubject vehicle is predicted to command at auction.

Alternatively or additionally, system 200 may determine auction pricingoptimization for a set of assets, generate a valuation report orvisualization (e.g., a view for presentation on the client device via auser interface) listing the optimized predicted prices with associatedtimes and locations, and communicate the valuation report orvisualization to a client device over a network, perhaps on demand orautomatically on a periodic basis.

According to some embodiments, to realize the robust price and profitoptimization mechanism, optimization engine 240 may implement thefollowing optimization engine algorithm and linear regression equations.Linear or non-linear regression models may be implemented. Forillustrative purposes, a linear regression is modeled below. Programmingtechniques necessary to compute the linear regression modeled below areknown to those skilled in the art.

Predicted price k at current location l and time t=0 (i.e.,p_(k, Current l,t=0)) where

p_(k,Current l,t=0)=Σβ1Location_(Current l)+β2Supply_(k,Current l)+β3Demand_(k,Current l)+β4CompSetSupply_(k-i,Current l)+Σβ5VINAttributes_(k,Current l)+Σβ6AuctionCharacteristics_(Current l)+β7p_(historic,k,Current l)+ρβ8Seasonality_(pk) +e   Equation [1]

Predicted price k at current location and time t+1, t+2, . . . t+n(i.e., p_(k, Current l,t>0)) where

p_(k,Current l,t>0)=Σβ1Location_(Current l)+β2Supply_(k,Current l)+β3Demand_(k,Current l)+β4CompSetSupply_(k-i,Current l)+Σβ5VINAttributes_(k,Current l)+Σβ6AuctionCharacteristics_(Current l)+β7p_(historic k,Current l)+Σβ8Seasonality_(pk) +e   Equation [2]

Predicted price k at new location l (≠current location l) and timet=0,t+1, . . . t+n) (i.e., p_(k, New l,t=0,t+1 . . . t+n)) where

p_(k,New l,t=0,t+1 . . . t+n)=Σβ1Location_(New)+β2Supply_(k,New l)+β3Supply_(Current l)+β4Demand_(k,New l)+β5CompSetSupply_(k-i,New l)+Σβ6VINAttributes_(k,New l)+β7Shipping_(k New l)+ρβ8AuctionCharacteristics_(New l)+β9p_(historic k,Current l)+β10p_(historic k,l=Σl≠Current l)+Σβ11Seasonality_(pk,t=0)   Equation [3]

Feature components (e.g., input variables with their regressioncoefficients) of the above Equations [1], [2], and [3] are described inTable 1 below. Skilled artisans appreciate that, although Equations [1],[2], and [3] are exemplified in view of auction pricing of a subjectvehicle, they can be adapted for pricing other types of assets. Thus,Equations [1], [2], and [3] and Table 1 are meant to be illustrative andnon-limiting.

TABLE 1 k Vehicle k at VIN level (with associated submake, series s andmake b) Current l, t = 0 Current auction location l (l = 1, 2, . . . m)and current time t (t = 0) Current l, t > 0 Current auction location l(l = 1, 2, . . . m) and time t (t > 0) New l, t = 0, t + 1, . . . t + nNew auction location l (l = 1, 2, . . . m, l ≠ current l) at time t (t =0, t + 1, . . . t + n) p_(k,New l,t=0,t+1, . . . t+n) Predicted price ateither new location l and time equal to either current (t = 0) ordifferent (t + 1, . . . t + n) p_(k,Current l,t=0) Predicted price atcurrent auction location and time (t = 0) p_(k,Current l,t>0) Predictedprice at current auction location and time (t > 0) ΣβLocation Auctionlocation l (1, 2, . . . m) and its influence on auction price. βrepresents regression coefficients. βSupply Supply or inventory ofvehicle k βDemand Demand of vehicle k (measured as estimated amount ofbidders at auction via proxy of using dealers around location buyingmodel k which, as a non-limiting example, can be stored as part ofmodels 128 in data store 122) βCompSetSupply Supply or inventory ofcompetitive set k-i βShipping Shipping cost from current location l tonew location l ΣβVINAttributes Vehicle attributes of vehicle k (i.e.,age, mileage, color, drive train, body style, fuel type)ΣβAuctionCharacteristics Characteristics of auction location (i.e., # oflanes, lane 1 or not, region, # of makes sold at auction)ΣβSeasonality_(p k) Monthly seasonality of historic prices βp_(historic)Historic auction prices for vehicle k at particular auction for timerange of t-n (i.e., last four weeks) >> default of binning structure butmay have to be redefined as sample size could be small Condition aOptimization conditions: p_(k,Current l,t>0) > p_(k,Current l,t=0) a)where price for vehicle k at current Condition b location l at t > 0 isgreater than price p_(k,New l,t=0,) > p_(k,Current l,t=0) for vehicle atcurrent location l at t = 0 Condition c b) price for vehicle k at newlocation l at p_(k,New l,t>0,) > p_(k,Current l,t=0) t = 0 is greaterthan price for vehicle k at current at t = 0 c) price for vehicle k atnew location l and t > 0 is greater than price for vehicle k at currentlocation l at t = 0

Following the example shown in FIG. 2, suppose a subject vehicle isexpected to command a price of $15,600 USD (e.g., output 214 from dataprocessing process 201) for auctioning the subject vehicle where it islocated (e.g., New York), optimization engine 240 may receive input 212and take into account the spatial and temporal factors 250 that affectthe subject vehicle at computing time and quantitatively determine thatthe subject vehicle may command a higher auction price of $16,000 at adifferent location (e.g., Texas) and at a time (e.g., March 13) that isdifferent from the current time. In this case, optimized price valuation260 from advanced data processing process 205 (which implements therobust price and profit optimization mechanism embodying the equationsdescribed above) represents an additional $400 potential gross profit tothe owner of the subject vehicle (e.g., an automotive dealer, anautomotive manufacturer, a fleet management company, a vehicle leasingcompany, an auction company, etc.).

Suppose the subject vehicle is one of five-hundred vehicles up forauction and $400 represents the average increase (i.e., optimizedauction pricing) determined by optimization engine 240, the differencebetween a regular pricing engine output and an optimized outcome byoptimization engine 240 can be quite substantial (in this example,$20,000). As the size of an asset portfolio increases, this substantialincrease can be magnified even more significantly.

While the robust price and profit optimization mechanism of advanceddata processing process 205 is realized by optimization engine 240 ofFIG. 2 in the above example, skilled artisans appreciate that otherimplementations are also possible. For example, FIG. 3 depicts adiagrammatic representation of pricing system 300 comprising functionalmodules 310, 320, 330, 340, and 350 configured for performing dataprocessing process 201 and advanced data processing process 205 of FIG.2 in topology 100 of FIG. 1 described above.

As a non-limiting example, data gathering module 310 may implement anembodiment of data gathering module 194, data processing module 320 mayimplement an embodiment of pricing engine 210, advanced data sciencemodule may implement an embodiment of optimization engine 240, networkclient interface module 340 may implement an embodiment of interfacemodule 192, and intelligent recommendation module 350 may implement anoptional recommendation engine, according to some embodiments.

FIG. 4 depicts a diagrammatic representation of pricing system 400having pricing engine 410 implementing data processing process 401 andoptimization engine 440 implementing advanced data processing process405. Data processing process 401 with input 412 and output 414 mayoperate similar to data processing process 201 with input 212 and output214 described above, while advanced data processing process 405 may berealized by various data analysis and computation components ofoptimization engine 440 described below.

As generally depicted in FIG. 4, data processing process 401 forpredicting the price that a subject vehicle is expected to command, forinstance, at auction or wholesale may include providing input 412containing vehicle information (e.g., VIN, trim, body style, etc.) ofthe subject vehicle. Pricing engine 410 may employ any suitable pricingtools to determine output 414 containing a price valuation representinga price that the subject vehicle is expected to command at auction orwholesale.

As a non-limiting example, suppose the subject vehicle is a 2012 CamryLE and a sale date in March 2015 was selected to predict price in April2015. For the purpose of illustration, and not of limitation, auctionrecords may include the following fields:

TABLE 2 FIELD NAME: FIELD DESCRIPTION: Sale Date MM/DD/YYYY = Mar. 9,2015 VIC NADA vehicle identification code—a copyrighted, proprietaryidentifier = 111637948 Make Make Description = Toyota Model Year 4 DigitMY = 2012 Submake Submake Description = Camry Series Series Description= Camry 4-cyl Bodystyle Bodystyle Description = Sedan 4D LE FuelType/Drive Fuel Type Description = Gasoline, Drive Type Type IdentifierDescription = FWD, AT Region Region Code = H (NADA descriptions) >>location Sale Price Actual Sale Price = $13,600 Mileage Actual OdometerMileage = 39,444 Color Color Description = Red Sale Type Sale Code = D(Dealer) VIN Vehicle Identification Number = 4T1BF1FK5CU000000

In some embodiments, optimization engine 440 can be configured toperform steps E1 . . . E5 (elements 441, 443, 445, 447, and 449) shownin FIG. 4.

E1 (441)—Determine a predicted price p_(k) for the subject vehicle(i.e., an example of an asset of interest) at a particular location l ateach of a plurality of times t. When time t equals the initial timepoint (t=0), Equation [1] is used.

Example of Equation [1]

p_(k,Current l,t=0)=ρβ1Location_(Current l)+β2Supply_(k,Current l)+β3Demand_(k,Current l)+β4CompSetSupply_(k-i,Current l)+Σβ5VINAttributes_(k,Current l)+Σβ6AuctionCharacteristics_(Current l)+β7p_(historic,k,Current l)+Σβ8Seasonality_(pk) +e

Suppose the location l is the current auction location, then l=1 isselected, given the following:

ΣβLocation=β1*Location 1(Location 1=1)+β11*Location 2(Location2=0)+β12*Location 3(Location 3=0)+β13*Location 4(Location4=0)+β14*Location 5(Location 5=0)+β15*Location 6(Location6=0)+β16*Location 7(Location 7=0)+β17*Location 8(Location8=0)+β18*Location 9(Location 9=0)+β19*Location 10(Location 10=0)

βSupply Current l(l1)=β2*supply of vehicle k (same body style)=β2*20(e.g., 20 vehicles at the current location have the same body style)

βDemand Current l(l1)=β3*15 (approximated by the number of dealers, inthis example, 15, that buy vehicle k/submakes in a predetermined radius,for instance, 30 miles within the current location)

βCompSetSupply location l(l1)=β4*30 (competitive body styles of k-imodels, for instance, Honda Accord, Ford Fusion, Chevy Malibu, HyundaiSonata, Nissan Altima, etc. auctioned off at the current location)

ΣβVINAttributes=Σβ5*VIN attributes (e.g., age, mileage, color, drivetrain, body style, fuel type)=β51*36 months(age)+β52*39,444(mileage)+β531*Red (=1 if red, else=0)+β532*White(=1 if white,else=0)+β533*Black(=1 if black, else=0)+β534*Silver(=1 if silver,else=0)+β541*FWD(=1 if FWD, else=0)+β542*AWD/4WD(=1 if AWD/4WD,else=0)+β551*AT(=1 if AT, else=0)+β552*MT(=1 if MT,else=0)+β561*Gasoline(=1 if Gas,=0 if not)+β562*Diesel(=1 if Diesel,=0if not)+β563*Electric(=1 if Electric,=0 if not)+β564*Hybrid(=1 ifHybrid,=0 if not)

ΣβAuctionCharacteristics Current l(l1)=β61*10 (the number of lanes, inthis example, 10, in auction house at the current location)+β62*1(=1 iflane 1,=0 if not),β631*West(=1 if West,=0 if not)+β632*East(=1 ifEast,=0 if not)+β633*South(=1 if South,=0 if not)+β634*North(=1 ifNorth,=0 if not)+β64*10 (the number of makes sold at the currentlocation l and on date i(t=0))

βp _(k historic Current l, t=0)=β7*$13,750 (average historic price ofvehicle k at the current location(l=1) in the last four weeks)

ΣβSeasonality_(pk,t=0)=β81*Jan(=1 if (t=0)=Jan)+β81*Feb+ . . .β84*Apr(=1 if (t=0)=April)+ . . . +β812*Dec

Values of these features can be determined in many ways. For example,the supply and demand can be determined using obtained data 124 fromdealers 130 a . . . 130 n and/or determined data 126 stored in datastore 122; a VIN decoder may be utilized to decode the VIN and determinethe values of the VIN attributes; auction characteristics at the currentlocation can be obtained from auction source(s) 186, obtained data 124,and/or determined data 126 stored in data store 122; and the averagehistoric price and seasonality can be determined using obtained data 124from dealers 130 a . . . 130 n, determined data 126, and/or models 128stored in data store 122. Furthermore, Boolean values may be used totransform non-numerical values (e.g., color, drive train, body style,fuel type, etc.).

Skilled artisans appreciate that statistical software refer tospecialized computer programs for analysis in statistics and that manysuch programs are suitable for statistical modeling. Computation ofEquation [1] exemplified above can be realized by leveragingcomputerized statistical analysis techniques known to those skilled inthe art. Linear regression or other suitable statistical modelsimplementing the special linear regression equations disclosed hereinmay be stored as part of models 128 in data store 122 such that they areaccessible by pricing systems implementing embodiments disclosed here.As a non-limiting example, applying Equation [1] at the current locationl (l=1) and time t (t=0) generates a first price of $14,000 such thatp_(k, Current l=1, t=0)=$14,000.

When time t is subsequent to the initial time point (t>0), Equation [2]is used. As a non-limiting example, applying Equation [2] at the currentlocation l (l=1) and time t (t=1) generates a second price of $13,955such that p_(k, Current l=1,t=1)=$13,955. Application of Equation [2]can be repeated, depending upon the number of time points desired. Thenumber of time points in this calculation can be a configurable number(e.g., depending upon a user-specified interval received via a userinterface, for instance, every three months for three years). For thesake of brevity, the predicted price p_(k) at the current location l=1at two different time points t=0 and t=1 are calculated in this example.

Once p_(k, Current l,t=0,t+1 . . . t+n) has been computed, optimizationengine 440 may proceed to perform the next step.

E2 (443) Determine a predicted price p_(k) at any other location atdifferent times (i.e., t=0 and t>0).

First, determine a predicted price p_(k) for each location other thanl=1 (i.e., new l=2, new l=3, etc.), at t=0. This is calculated usingEquation [3]. Example outputs are as follows:

p _(k,new l=2,t=0)=$13,800

p _(k,new l=3,t=0)=$13,560

p _(k,new l=4,t=0)=$13,320

p _(k,new l=5,t=0)=$13,100

p _(k,new l=6,t=0)=$13,450

p _(k,new l=7,t=0)=$12,900

p _(k,new l=8,t=0)=$13,120

p _(k,new l=9,t=0)=$13,220

p _(k,new l=10,t=0)=$13,950

Next, determine a predicted price p_(k) for each location other than l=1(i.e., new l=2, new l=3, etc.), for t+1 (t=1). Again using Equation [3],optimization engine 440 can generate the following outputs:

p _(k,new l=2,t=1)=$13,720

p _(k,new l=3,t=1)=$13,145

p _(k,new l=4,t=1)=$13,500

p _(k,new l=5,t=1)=$13,650

p _(k,new l=6,t=1)=$12,800

p _(k,new l=7,t=1)=$13,670

p _(k,new l=8,t=1)=$13,890

p _(k,new l=9,t=1)=$13,620

p _(k,new l=10,t=1)=$13,950

As an example, p_(k, new l=2, t=1)=$13,720 can be determined usingEquation [3] as follows.

ΣβLocation=β1*Location 1(Location 1=0)+β11*Location 2(Location2=1)+β12*Location 3(Location 3=0)+β13*Location 4(Location4=0)+β14*Location 5(Location 5=0)+β15*Location 6(Location6=0)+β16*Location 7(Location 7=0)+β17*Location 8(Location8=0)+β18*Location 9(Location 9=0)+β19*Location 10(Location 10=0)

βSupply new l(l2)=β2*supply of vehicle k (same body style)=β2*50 (i.e.,there are 50 vehicles having the same body style at the new locationl=2)

βSupply Current l(l1)=β3*supply of vehicle k (same body style)=β2*20(i.e., there are 20 vehicles having the same body style at the currentlocation l=1)

βDemand new l(l2)=β4*10 (approximated by the number of dealers buyingvehicle k/submakes in particular radius of 30 miles of the new locationl=2)

βCompSetSupply new l(l2)=β5*40 (competitive body styles of k-i models,e.g., Honda Accord, Ford Fusion, Chevy Malibu, Hyundai Sonata, NissanAltima, etc., auctioned off at the new location l=2 and date i(t=1))

ΣβVINAttributes=β6*VIN attributes (age, mileage, color, drive train,body style, fuel type)=β61*36 months+β62*39,444+β631*Red(=1 if red,else=0)+β632*White(=1 if white, else=0)+β633*Black(=1 if black,else=0)+β634*Silver(=1 if silver, else=0)+β641*FWD(=1 if FWD,else=0)+β642*AWD/4WD(=1 if AWD/4WD, else=0)+β651*AT(=1 if AT,else=0)+β652*MT (=1 if MT, else=0)+β661*Gasoline(=1 if Gasoline,=0 ifnot)+β662*Diesel(=1 if Diesel,=0 if not)+β663*Electric(=1 if Electric,=0if not)+β664*Hybrid(=1 if Hybrid,=0 if not)

βShipping_(k New l (l2))=β7*$500 (shipping cost getting it from thecurrent location l=1 to the new location l=2)

ΣβAuctionCharacteristics new l(l2)=β81*10 (representing the number oflanes in the auction house at the new location l=2)+β82*1(=1 if lane1,=0 if not),β831*West(=1 if West,=0 if not)+β832*East(=1 if East,=0 ifnot)+β833*South(=1 if South,=0 if not)+β834*North(=1 if North,=0 ifnot)+β84*10 (the number of makes sold at the new location l=2 and t=1)

βp _(k historic Current l (l1), t=0)=β9*$13,750 (average historic priceof vehicle k at the current location (l=1) in the last four weeks)

βp _(k historic New l (l2), t=1)=β9*$13,250 (average historic price ofvehicle k at the new location (l=2) in the last four weeks)

ΣβSeasonality_(pk,t=1)=β111*Jan(=1 if(t=1)=Jan)+β112*Feb+β114*Apr+β115*May(=1 if (t=1)=May)+ . . . β1112*Dec

From steps E1 and E2, optimization engine 440 now has predicted pricep_(k) at different locations l (e.g., l=1, l=2, etc.) at different timest (e.g., t=1, t=2, etc.). Thus, optimization engine 440 can proceed tocompute a price matrix based on outputs from steps E1 and E2.

E3 (445)—Compute Price Matrix. As illustrated in the example pricematrix provided in Table 3 below, the price matrix is computed usingoutputs from E1 and E2. The computed price matrix contains an array ofvalues corresponding to the plurality of locations and time pointsdetermined from E1 and E2. Each value in the array is associated with aspecific location at a specific point in time. The computed price matrixmay be persisted (e.g., stored in data store 122 of FIG. 1) and/orstored in memory in a transient way (e.g., during a network transactionor session between a network site supported by pricing system 400 (andhence optimization engine 440) and a client device from which a requestfor pricing the subject vehicle is originated (e.g., via a userinterface of the network site) and received by pricing system 400).

In the non-limiting example of Table 3, the different locations arerepresented in the price matrix as rows and the different time pointsare represented in the price matrix as columns. Skilled artisansappreciate that other arrangements of the computed values may also bepossible.

TABLE 3 PRICE MATRIX T0 T1 T . . . L1 $14,000 $13,955p_(k,Current l=1,t=n) L2 $13,800 $13,720 p_(k,New l=2,t=n) L3 $13,560$13,145 p_(k,new l=3,t=n) L4 $13,320 $13,500 p_(k,new l=4,t=n) L5$13,100 $13,650 p_(k,new l=5,t=n) L6 $13,450 $12,800 p_(k,new l=6,t=n)L7 $12,900 $13,670 p_(k,new l=7,t=n) L8 $13,120 $13,890p_(k,new l=8,t=n) L9 $13,220 $13,620 p_(k,new l=9,t=n) L10 $13,950$13,950 p_(k,new l=10,t=n) L . . . p_(k,l=m,t=0) p_(k,l=m,t=1)p_(k,new l=m,t=n)

E4 (447)—Compare price matrix and determine whether optimizationconditions a (482), b (484), c (486) are true or false. For example:

a. When the predicted price p_(k) at the current location l=1 when t>0(e.g., t+1, t+2, t+n) is not greater than the predicted price p_(k) atthe current location l=1 at t=0, this optimization condition is not met;go to b. When the predicted price p_(k) at the current location l=1 whent>0 is greater than the predicted price p_(k) at the current locationl=1 at t=0, this optimization condition is met and the predicted pricep_(k) at the current location l=1 when t>0 is identified (496). Thiscomparison can be repeated for the predicted price p_(k) at each timepoint in a plurality of time points of interest, all at the currentlocation l=1, relative to the predicted price p_(k) at the currentlocation l=1 at the initial point in time t=0.

b. When the predicted price p_(k) at a new location l>1 at t=0 is notgreater than the predicted price p_(k) at the current location l=1 att=0, this optimization condition is not met; go to c. When the predictedprice p_(k) at a new location l>1 at t=0 is greater than the predictedprice p_(k) at the current location l=1 at t=0, this optimizationcondition is met and the predicted price p_(k) at a new location l>1 att=0 is identified (494). This comparison can be repeated for thepredicted price p_(k) at each new location in a plurality of newlocations of interest, all at the same time t=0, relative to thepredicted price p_(k) at the current location l=1 at the same time t=0.

c. When the predicted price p_(k) at a new location l>1 when t>0 is notgreater than the predicted price p_(k) at the current location l=1 att=0, this optimization condition is not met and the predicted pricep_(k) at the current location at t=0 is identified (490). When thepredicted price p_(k) at a new location l>1 when t>0 is greater than thepredicted price p_(k) at the current location l=1 at t=0, thisoptimization condition is met and the predicted price p_(k) at a newlocation l>1 when t>0 is identified (492).

Essentially, multiple types of comparisons are performed in E4. A firsttype of comparison is keyed to location—holding the current location l=1and attempting to identify better predicted price(s) in the price matrixat any time point (t>0) subsequent to the initial time point (t=0) atthe same current location (l=1). A second type of comparison is keyed totime—holding the time constant and attempting to identify betterpredicted price(s) in the price matrix at any new location. A third typeof comparison is keyed to a specific location (e.g., the currentlocation l=1) at a specific time (e.g., the initial time t=0) andattempts to identify better predicted price(s) in the price matrix atany time point (t>0) subsequent to the initial time point (t=0) at anynew location.

When multiple values can be identified from these comparisons (e.g.,490, 492, 494, and 496), a determination can be made as to which valuerepresents the best, optimized price at which the subject vehicle cancommand, for instance, at auction or wholesale. Based on thisdetermination, a recommendation may be made in the next step E5.

E5—Predicted Price Output

Following the above example price matrix, none of the optimizationconditions a, b, or c is met. Accordingly, the final, optimizedpredicted price p_(k) in regards to location and time is the same as thepredicted price p_(k) at the current location l (l=1) at the initialtime t (t=0), which is $14,000 in this example.

In this way, optimization engine 440 may receive input 412 and operateto evaluate spatial factors (e.g., auction locations 455, 456, 457, 458,459, 460, 461, 462, 463, 464, 465, 470, etc.) and temporal factors(e.g., auction times 430, 435, 437, etc.) to determine their effects onpredicted price valuation and ensure a quantitatively optimized priceprediction for a subject vehicle.

Skilled artisans appreciate that Equations [1], [2], and [3] used incomputing the predicted price p_(k) in steps (E1 . . . E5) describedabove can be expressed in other ways. For example, a generalizedformulation for constructing a linear regression model in accordancewith a representative embodiment may comprise the following linearexpression as implemented on a computing device or system for numericalanalysis and optimization:

{circumflex over (P)} _(φ,l,t)=β₀+Σβ₁ L _(φ,l,t)+β₂ S _(φ,l,t)+β₃ D_(φ,l,t)+β₄ S* _(φ-δ,l,t)+Σβ₅ V _(φ,l,t)+β₆ A _(φ,l,t)+β₇ H _(φ,l,t)+Σβ₈T _(φ,l,t)+ε_(φ,l,t)

where:

{circumflex over (P)}_(φ,l,t) is a predicted auction price {circumflexover (P)} for vehicle φ at location l at time t;

L_(φ,l,t) represents an auction location explanatory variable formodeling how the geographic location of the auction affects thepredicted auction price {circumflex over (P)} for vehicle φ at locationl at time t;

S_(φ,l,t) represents a supply (e.g., inventory) explanatory variable formodeling how supply affects the predicted auction price {circumflex over(P)} for vehicle φ at location l at time t;

D_(φ,l,t) represents a demand explanatory variable (e.g., measured as anestimated number of bidders in attendance at auction that, in arepresentative embodiment, may be determined via proxy analysis ofdealers in the geographic vicinity of location l buying vehicle φ) formodeling how demand affects the predicted auction price {circumflex over(P)} for vehicle φ at location l at time t;

S*_(φ-δ,l,t) represents a competitive supply (e.g., competitiveinventory) explanatory variable for modeling how supply of a competitiveset of vehicles φ−δ affects the predicted auction price {circumflex over(P)} for vehicle φ at location l at time t;

V_(φ,l,t) represents a vehicle attribute (e.g., year of manufacture,make, model, trim, mileage, color, drive train, body style, fuel type,miles-per-gallon, etc.) explanatory variable for modeling how vehicleattributes affect the predicted auction price {circumflex over (P)} forvehicle φ at location l at time t;

A_(φ,l,t) represents an auction characteristic (e.g., the number oflanes, whether vehicle φ will be in a particular lane or not, the numberof identical or similar makes sold at auction, etc.) explanatoryvariable for modeling how auction characteristics affect the predictedauction price {circumflex over (P)} for vehicle φ at location l at timet;

H_(φ,l,t) represents a historic auction price (e.g., previous auctionprices commanded by vehicle φ at a particular auction during a previoustime period—e.g., the last four weeks, etc.) explanatory variable formodeling how historic auction prices affect the predicted auction price{circumflex over (P)} for vehicle φ at location l at time t;

T_(φ,l,t) represents a seasonal historic auction price (e.g., previousauction prices commanded by vehicle φ at a particular auction during aparticular season—e.g., mid-Spring, etc.) explanatory variable formodeling how seasonal historic auction prices affect the predictedauction price {circumflex over (P)} for vehicle φ at location l at timet;

β₀ represents the intercept of the linear regression equation (i.e., thevalue of the criterion when the predictor is equal to zero);

{β₁,β₂,β₃,β₄,β₅,β₆,β₇,β₈} represent linear regression coefficients thatare numerically optimized; and

ε_(φ,l,t) is the standard error term commonly associated with linearregression.

Population of, e.g., historical auction price data into the above linearregression model may proceed with subsequent optimization of theparameters β_(i) to tune the model to conform to the populatedhistorical data. In a representative embodiment, such conformationalcorrespondence may be achieved (or otherwise approximated) via, e.g.,minimization of the ordinary least squares difference as betweenhistorical auction price data and the modeled linear function. Anynumerical optimization method, whether now known or hereafter derived inthe art, may be alternatively, conjunctively or sequentially employed toachieve a substantially similar result. See, for example, NumericalRecipes: The Art of Scientific Computing, Third Edition (2007), 1256pp., Cambridge University Press, ISBN-10: 0521880688, which isincorporated by reference herein. For large data sets, optimization mayproceed by a directional derivative and/or gradient approach, e.g., toaccelerate conformational convergence of the linear function to thehistorical auction price data. Historical auction data used to computeand optimize prices may be obtained or received from various sourcessuch as auction source(s) 186 communicatively connected to vehicle datasystem 120 described above with reference to FIG. 1.

The above-defined expression may be modified and variously implementedto incorporate particular spatial and/or temporal factors. For example:

for evaluation of data corresponding to a current location l₀ and thecurrent time t₀ . . .

${\hat{P}}_{\varphi,l,t} = {\beta_{0} + {\sum{\beta_{1}L_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}}} + {\beta_{2}S_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}} + {\beta_{3}D_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}} + {\beta_{4}S_{\begin{matrix}{\varphi - \delta} \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}^{*}} + {\sum{\beta_{5}V_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}}} + {\sum{\beta_{6}A_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}}} + {\beta_{7}H_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}} + {\sum{\beta_{8}T_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}}} + ɛ_{\varphi,I_{0},t_{0}}}$

for evaluation of data corresponding to a current location l₀ and afuture time t_(n) . . .

${\hat{P}}_{\varphi,l,t} = {\beta_{0} + {\sum{\beta_{1}L_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}}} + {\beta_{2}S_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}} + {\beta_{3}D_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}} + {\beta_{4}S_{\begin{matrix}{\varphi - \delta} \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}^{*}} + {\sum{\beta_{5}V_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}}} + {\sum{\beta_{6}A_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}}} + {\beta_{7}H_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}} + {\sum{\beta_{8}T_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}}} + ɛ_{\varphi,I_{0},t_{n}}}$

for evaluation of data corresponding to a new location l′ and thecurrent time t₀ . . .

${\hat{P}}_{\varphi,l,t} = {\beta_{0} + {\sum{\beta_{1}L_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}}} + {\beta_{2}S_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}} + {\beta_{3}D_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}} + {\beta_{4}S_{\begin{matrix}{\varphi - \delta} \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}^{*}} + {\sum{\beta_{5}V_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}}} + {\sum{\beta_{6}A_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}}} + {\beta_{7}H_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}} + {\sum{\beta_{6}T_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}}} + ɛ_{\varphi,I^{\prime},t_{0}}}$

and for evaluation of data corresponding to a new location l′ and afuture time t_(n) . . .

${\hat{P}}_{\varphi,l,t} = {\beta_{0} + {\sum{\beta_{1}L_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{n}}\end{matrix}}}} + {\beta_{2}S_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{n}}\end{matrix}}} + {\beta_{3}D_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{n}}\end{matrix}}} + {\beta_{4}S_{\begin{matrix}{\varphi - \delta} \\{l = l^{\prime}} \\{t = t_{n}}\end{matrix}}^{*}} + {\sum{\beta_{5}V_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{n}}\end{matrix}}}} + {\sum{\beta_{6}A_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{n}}\end{matrix}}}} + {\beta_{7}H_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{n}}\end{matrix}}} + {\sum{\beta_{6}T_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{n}}\end{matrix}}}} + ɛ_{\varphi,I^{\prime},t_{0}}}$

If a linear regression model is formulated to account for spatialfactors associated with, for example, an auction being remotely locatedfrom the current location of a vehicle (e.g., new location l′), a modelin accordance with the following representative embodiment may bealternatively, conjunctively or sequentially employed:

${\hat{P}}_{\varphi,l,t} = {\beta_{0} + {\sum{\beta_{1}L_{\begin{matrix}\varphi \\{l = l^{\prime}} \\t\end{matrix}}}} + {\beta_{2}S_{\begin{matrix}\varphi \\{l = l^{\prime}} \\t\end{matrix}}} + {\beta_{3}D_{\begin{matrix}\varphi \\{l = l^{\prime}} \\t\end{matrix}}} + {\beta_{4}S_{\begin{matrix}{\varphi - \delta} \\{l = l^{\prime}} \\t\end{matrix}}^{*}} + {\sum{\beta_{5}V_{\begin{matrix}\varphi \\{l = l^{\prime}} \\t\end{matrix}}}} + {\sum{\beta_{6}A_{\begin{matrix}\varphi \\{l = l^{\prime}} \\t\end{matrix}}}} + {\beta_{7}H_{\begin{matrix}\varphi \\{l = l^{\prime}} \\t\end{matrix}}} + {\sum{\beta_{8}T_{\begin{matrix}\varphi \\{l = l^{\prime}} \\t\end{matrix}}\beta_{9}M_{\begin{matrix}\varphi \\{l = l^{\prime}} \\t\end{matrix}}}} + ɛ_{\varphi,I^{\prime},t}}$

where the term

$M_{\begin{matrix}\varphi \\{l = l^{\prime}} \\t\end{matrix}}$

represents a shipping/storage cost (e.g., associated with moving vehicleφ to location l′ and/or storing vehicle φ at location l′) explanatoryvariable for modeling how shipping and/or storage costs affect thepredicted auction price {circumflex over (P)} for vehicle φ at locationl′ at time t.

Referring to FIGS. 4 and 5 and following the above automotive example, arepresentative embodiment of a method for quantitatively optimizedauction price prediction may proceed with determination (441, 510) of aprice prediction for a current auction location l₀ in accordance withthe following linear regression model:

${\hat{P}}_{\varphi,l,t} = {\beta_{0} + {\sum{\beta_{1}L_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}}} + {\beta_{2}S_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}} + {\beta_{3}D_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}} + {\beta_{4}S_{\begin{matrix}{\varphi - \delta} \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}^{*}} + {\sum{\beta_{5}V_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}}} + {\sum{\beta_{6}A_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}}} + {\beta_{7}H_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}} + {\sum{\beta_{8}T_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{0}}\end{matrix}}}} + ɛ_{\varphi,I_{0},t_{0}}}$

using historical auction data comprising a plurality of records (seee.g., Table 1).

Such determination (441, 510) of a price prediction for a currentauction location l₀ may be implemented in any application suitablyconfigured to perform linear regression analysis, such as, for example:a statistics package or library in a programming language, SAS (e.g.,Proc reg or proc glm procedure), R (e.g., glm function; see, e.g.,http://www.ats.ucla.edu/stat/r/dae/logit.htm), SPSS, Minitab, and/or thelike. These terms are known to skilled artisans and thus are not furtherdescribed herein for the sake of brevity.

Representative coding nomenclature for implementation of linearregression analysis to determine price predictions for a plurality oflocations and other factors may comprise, for example:

ΣβLocation=β1*Location 1(Location 1=1)+β11*Location 2(Location2=0)+β12*Location 3(Location 3=0)+β13*Location 4(Location4=0)+β14*Location 5(Location 5=0)+β15*Location 6(Location6=0)+β16*Location 7(Location 7=0)+β17*Location 8(Location8=0)+β18*Location 9(Location 9=0)+β19*Location 10(Location 10=0)

βSupply Current l(l1)=β2*supply of vehicle k (same bodystyle)=β2*20

βDemand Current l(l1)=β3*15 (approximated by the number of dealersbuying vehicle k/submakes in particular radius of 30 miles of thecurrent location)

βCompSetSupply location l(l1)=β4*30 (competitive body styles of k-imodels, e.g., Honda Accord, Ford Fusion, Chevy Malibu, Hyundai Sonata,Nissan Altima, etc., auctioned off at the current location l=1 and timet=0

ρΣVINAttributes=β5*VIN attributes (age, mileage, color, drive train,bodystyle, fuel type)=β51*36 months+β52*39,444+β531*Red(=1 if red,else=0)+β532*White(=1 if white, else=0)+β533*Black(=1 if black,else=0)+β534*Silver(=1 if silver, else=0)+β541*FWD(=1 if FWD,else=0)+β542*AWD/4WD(=1 if AWD/4WD, else=0)+β551*AT(=1 if AT,else=0)+β552*MT (=1 if MT, else=0)+β561*Gas(=1 if Gas,=0 ifnot)+β562*Diesel(=1 if Diesel,=0 if not)+β563*Electric(=1 if Electric,=0if not)+β564*Hybrid(=1 if Hybrid,=0 if not)

ΣβAuctionCharacteristics current l(l1)=β61*10 (representing the numberof lanes in the auction house at the current location l=1)+β62*1(=1 iflane 1,=0 if not),β631*West(=1 if West,=0 if not)+β632*East(=1 ifEast,=0 if not)+β633*South(=1 if South,=0 if not)+β634*North(=1 ifNorth,=0 if not)+β64*10(# of makes sold at current location l(l=1) andon date i(t=0)).

Thereafter, the representatively disclosed method may determine (443,520) a price prediction for a remote auction location l′ in accordancewith the following linear regression model:

${\hat{P}}_{\varphi,l,t} = {\beta_{0} + {\sum{\beta_{1}L_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}}} + {\beta_{2}S_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}} + {\beta_{3}D_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}} + {\beta_{4}S_{\begin{matrix}{\varphi - \delta} \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}^{*}} + {\sum{\beta_{5}V_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}}} + {\sum{\beta_{6}A_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}}} + {\beta_{7}H_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}} + {\sum{\beta_{6}T_{\begin{matrix}\varphi \\{l = l^{\prime}} \\{t = t_{0}}\end{matrix}}}} + ɛ_{\varphi,I^{\prime},t_{0}}}$

The procedure for determining (443, 520) price predictions for a remoteauction location l′ may be iteratively implemented for a plurality oflocations (e.g., 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 470,etc.), for instance, to compute (445, 530) a component of a price matrix(472) comprising a corresponding plurality of location-specific auctionprice prediction values (e.g., rows L1, L2, . . . , etc.) at differenttimes.

For each of the plurality of location-specific auction price predictionvalues, the representatively disclosed method may compute (445, 530)additional information to include in the price matrix (472)corresponding to an associated plurality of time perturbed pricepredictions for a given location in accordance with the followinglogistic regression model:

${\hat{P}}_{\varphi,l,t} = {\beta_{0} + {\sum{\beta_{1}L_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}}} + {\beta_{2}S_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}} + {\beta_{3}D_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}} + {\beta_{4}S_{\begin{matrix}{\varphi - \delta} \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}^{*}} + {\sum{\beta_{5}V_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}}} + {\sum{\beta_{6}A_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}}} + {\beta_{7}H_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}} + {\sum{\beta_{8}T_{\begin{matrix}\varphi \\{l = l_{0}} \\{t = t_{n}}\end{matrix}}}} + ɛ_{\varphi,I_{0},t_{n}}}$

The procedure for determining and associated a plurality of timeperturbed price predictions for a given location may be iterativelyimplemented for a plurality of times (e.g., 430, 435, 437, etc.), forinstance, to compute (445, 530) a component of a price matrix comprisinga corresponding plurality of time-specific auction price predictionvalues (e.g., columns 474, 476, 478) at different locations.

Representative coding nomenclature for implementation of linearregression analysis to determine price predictions for a plurality oftime perturbations may comprise, for example:

βp _(k historic current l (l1), t=0)=β9*$13,750 (average historic priceof vehicle k at current location (l1) in last four weeks)

βp _(k historic new l (l2), t=1)=β9*$13,250 (average historic price ofvehicle k at new location (l2) in last four weeks)

ΣβSeasonality_(pk,t=1)=β111*Jan(=1 if t=0=Jan)+β112*Feb+ . . .0.4114*Apr+β115*May+ . . . β1112*Dec

Iterative application of the above-defined procedures (i.e., regressionanalysis for a plurality of locations at different times) may produce aprice matrix as exemplified in Table 4 below.

TABLE 4 PRICE MATRIX t₀ t₁ . . . t_(n) l₁ $14,000 $13,955 {circumflexover (P)}_(φ,l) ₁,t_(n) l₂ $13,800 $13,720 {circumflex over (P)}_(φ,l)₂,t_(n) l₃ $13,560 $13,145 {circumflex over (P)}_(φ,l) ₃,t_(n) l₄$13,320 $13,500 {circumflex over (P)}_(φ,l) ₄,t_(n) l₅ $13,100 $13,650{circumflex over (P)}_(φ,l) ₅,t_(n) l₆ $13,450 $12,800 {circumflex over(P)}_(φ,l) ₆,t_(n) l₇ $12,900 $13,670 {circumflex over (P)}_(φ,l)₇,t_(n) l₈ $13,120 $13,890 {circumflex over (P)}_(φ,l) ₈,t_(n) l₉$13,220 $13,620 {circumflex over (P)}_(φ,l) ₉,t_(n) l₁₀ $13,950 $13,950{circumflex over (P)}_(φ,l) ₁₀,t_(n) l_(m) {circumflex over (P)}_(φ,l)_(m),t₀ {circumflex over (P)}_(φ,l) _(m),t₁ {circumflex over (P)}_(φ,l)_(m),t_(n)

In a representative embodiment, auction price optimization may proceed,for example, in accordance with analysis of price matrix elements (447,540) with respect to the following:

Condition A (element 482 of FIG. 4): {circumflex over (P)}_(φ,l,t)_(n) >{circumflex over (P)}_(φ,l,t) _(z) (e.g., comparing a predictedauction price associated with a first time t_(z) to a predicted priceassociated with a second time t_(n), for selling vehicle φ at a givenlocation l); and

Condition B (element 484 of FIG. 4): {circumflex over (P)}_(φ,l) _(q)_(,t)>{circumflex over (P)}_(φ,l) _(p) _(,t) (e.g., comparing apredicted auction price associated with a first location l_(p) to apredicted price associated with a second location l_(q) for sellingvehicle φ at given time t); and

Condition C (element 486 of FIG. 4): {circumflex over (P)}_(φ,l) _(q)_(,t) _(n) >{circumflex over (P)}_(φ,l) _(p) _(,t) _(z) (e.g., comparinga predicted auction price associated with a first time t_(z) and a firstlocation l_(p) to a predicted price associated with a second time t_(n),and a second location l_(q) for selling vehicle φ).

In various representative practical applications of the disclosed methodfor auction price prediction and optimization, it may be preferable tokeep a vehicle at a given location until a favorable time window opensfor commissioning the vehicle to auction, rather than ship the vehicleto a distant location for sale at auction. Accordingly, a hierarchy ofconditional analysis may be implemented to bias a preference for keepinga vehicle at a given location for a period of time rather than shippingthe vehicle to a distant location in accordance with the following:

Firstly, looping over time perturbations for the current location of thevehicle, if the result of Condition A (482) is true (i.e., there existsa time for which an acceptable maximum auction price is predicted to beachievable at the current location of the vehicle), then optimallocation-keyed auction price 496 is identified, subject to remainingrecursive evaluation (540) of the price matrix with respect to time.

Next in sequence, looping over different locations for a given time, ifthe result of Condition B (484) is true (i.e., there exists a locationfor which an acceptable maximum auction price is predicted to beachievable at the given time), then optimal time-keyed auction price 494is identified, subject to remaining recursive evaluation (540) of theprice matrix with respect to location.

Next in sequence, looping over different locations and different times,if the result of Condition C (486) is true (there exists a time andplace for which an acceptable maximum auction price is predicted to beachieved), then optimal time-and-location-keyed auction price 492 isidentified, subject to remaining recursive evaluation (540) of the pricematrix with respect to location and time.

In this example, since none of optimization conditions A (482), B (484),or C (486) is true, the baseline price comparator is reported as theoptimal auction price 490.

Iterative application of analysis 447, 540 for all matrix elements (or asubstantial portion thereof) may be implemented to quantitativelypredict (449) an optimal predicted price that may be commanded byvehicle φ at auction with respect to varying spatial factors (e.g.,geographic auction locations 455, 456, 457, 458, 459, 460, 461, 462,463, 464, 465, 470, etc.) and/or temporal factors (e.g., auction times430, 435, 437, etc.) and display (550) the same to a client and/or userof pricing system 400 or method 500.

When one of the optimization conditions A (482), B (484), or C (486)might return true, optimization engine 400 may operate to determine(560) the most optimal outcome based the optimization condition that hasbeen met and in view of the spatial and/or temporal factors meeting theoptimization condition. Optimization engine 400 may further make arecommendation (570) as to where and when to sell the subject vehicle inaccordance with the optimization condition that has been met. Therecommendation can be communicated to a client-facing interface (e.g.,generated by an embodiment of interface module 192 of vehicle datasystem 120 or network client interface module 340 of pricing system 300)over the network for presentation (550) on the client device.

Accordingly, various embodiments in accordance with representativelydisclosed aspects provide systems and methods capable of predicting theprice that a particular vehicle will command at a particular auction ata particular time within, e.g., a 95% confidence interval.

FIG. 6 depicts a diagrammatic representation of one example embodimentof a data processing system suitable for implementing embodimentsdisclosed herein. As shown in FIG. 6, data processing system 600 mayinclude one or more processor(s) 601 coupled to one or more userinput/output (I/O) devices 603 and memory devices 605. Examples of I/Odevices 603 may include, but are not limited to, keyboards, displays,monitors, touch screens, printers, electronic pointing devices such asmice, trackballs, styluses, touch pads, or the like. Examples of memorydevices 605 may include, but are not limited to, hard drives (HDs),magnetic disk drives, optical disk drives, magnetic cassettes, tapedrives, flash memory cards, random access memories (RAMs), read-onlymemories (ROMs), smart cards, etc. Data processing system 600 can becoupled to display 611, data storage device 613 and various peripheraldevices (not shown), such as printers, plotters, speakers, etc. throughI/O devices 603. Data processing system 600 may also be coupled toexternal computers or other devices through network interface 607,wireless transceiver 609, or other means that is coupled to a networksuch as a local area network (LAN), wide area network (WAN), or theInternet.

Although the invention has been described with respect to specificembodiments herein, these embodiments are merely illustrative, and notrestrictive of the disclosure. The description herein of representativeembodiments of the invention, including the description in the Abstractand Summary, is not intended to be exhaustive or to limit the disclosureto the precise forms described herein (and in particular, the inclusionof any particular embodiment, feature or function within the Abstract orSummary is not intended to limit the scope of the disclosure to suchembodiment, feature or function). Rather, the description is intended todescribe illustrative embodiments, features and functions in order toprovide a person of ordinary skill in the art context to understand thedisclosure without limiting the disclosure to any particularlyembodiment, feature or function, including any such embodiment featureor function described in the Abstract or Summary. While specificembodiments of, and examples for, are described herein for illustrativepurposes, various substantially equivalent modifications are possiblewithin the spirit and scope of the disclosure, as those skilled in therelevant art will recognize and appreciate. As indicated, suchmodifications may be made to the disclosure in view of the foregoingdescription of representative embodiments and are to be included withinthe spirit and scope of the disclosure. Thus, while variousrepresentative embodiments have been described herein, a latitude ofmodification, various changes and substitutions are intended forinclusion in the disclosure, and it will be appreciated that in someinstances some features of various representative embodiments may beemployed without corresponding use of other features without departingfrom the scope and spirit of the disclosure as set forth herein.Therefore, many modifications may be made to adapt a particularsituation or material to the essential scope and spirit of thedisclosure.

Reference throughout this specification to “one embodiment”, “anembodiment”, or “a specific embodiment” or contextual variants thereof,means that a particular feature, structure, or characteristic describedin connection with the subject embodiment is included in at least oneembodiment and may not necessarily be present in all embodiments.Accordingly, respective appearances of the phrases “in one embodiment”,“in an embodiment”, or “in a specific embodiment” or contextual variantsthereof, in various places throughout this specification, are notnecessarily referring to the same or even related embodiments.Furthermore, the particular features, structures, or characteristics ofany particular embodiment may be combined in any suitable manner withone or more other embodiments. It will be understood that othervariations and modifications of the representative embodiments describedand illustrated herein are possible in light of the teachings herein andare to be considered as part of the spirit and scope of the disclosure.

In the description herein, numerous specific details are provided, suchas examples of components and/or methods, to provide a thoroughunderstanding of various representative embodiments. One skilled in therelevant art will recognize, however, that a particular embodiment maybe able to be practiced without one or more of the specific detailsrecited, or with other apparatuses, systems, assemblies, methods,components, materials, parts, and/or the like. In other instances,well-known structures, components, systems, materials, or operations arenot specifically shown or described in detail to avoid unnecessarilyobscuring aspects of embodiments of the invention. While the inventionmay be illustrated with respect to a particular embodiment, this is notand does not limit the invention to any specific embodiment, and aperson of ordinary skill in the art will recognize that additionalembodiments are readily understandable and contemplated and included inthis disclosure.

Representative embodiments discussed herein may be implemented in acomputer communicatively coupled to a network (for example, theInternet), another computer, or in a standalone computer. As is known tothose skilled in the art, a suitable computer can include a centralprocessing unit (“CPU”), at least one read-only memory (“ROM”), at leastone random access memory (“RAM”), at least one hard drive (“HD”), andone or more input/output (“I/O”) device(s). The I/O devices may includea keyboard, monitor, printer, electronic pointing device (for example,mouse, trackball, stylus, touch pad, etc.), and/or the like.

ROM, RAM, and HD are computer memories for storing computer-executableinstructions executable by the CPU or capable of being compiled orinterpreted to be executable by the CPU. Suitable computer-executableinstructions may reside on a computer readable medium (e.g., ROM, RAM,and/or HD), hardware circuitry or the like, or any combination thereof.Within this disclosure, the term “computer readable medium” is notlimited to ROM, RAM, and HD and can include any type of data storagemedium that can be read by a processor, whether now known or hereafterderived in the art. For example, a computer-readable medium may refer toa data cartridge, a data backup magnetic tape, a floppy diskette, aflash memory drive, an optical data storage drive, a CD-ROM, ROM, RAM,HD, and/or the like. The processes described herein may be implementedin suitable computer-executable instructions that may reside on acomputer readable medium (for example, a disk, CD-ROM, a memory, etc.).Alternatively, conjunctively or sequentially, computer-executableinstructions may be stored as software code components on a directaccess storage device array, magnetic tape, floppy diskette, opticalstorage device, and/or other appropriate computer-readable medium orstorage device.

Any suitable programming language may be used, individually or inconjunction with another programming language, to implement theroutines, methods or programs of various representative embodimentsdescribed herein, including C, C++, Java, JavaScript, HTML, or any otherprogramming or scripting language, etc. Other software/hardware/networkarchitectures may be used. For example, the functions of variouslydisclosed embodiments may be implemented on one computer orshared/distributed among two or more computers in or across a network.Communications between computers implementing representative embodimentscan be accomplished using any electronic, optical, radio frequencysignals, or other suitable methods and/or tools of communication incompliance with known network protocols.

Different programming techniques may be employed such as procedural orobject oriented. Any particular routine can execute on a singlecomputer-processing device or multiple computer processing devices, asingle computer processor or multiple computer processors. Data may bestored in a single storage medium or distributed through multiplestorage media, and may reside in a single database or multiple databases(or other data storage techniques). Although the steps, operations, orcomputations may be presented in a specific order, this order may bechanged in different embodiments. In some embodiments, to the extentmultiple steps are shown as sequential in this specification, somecombination of such steps in alternative or conjunctive embodiments maybe performed at substantially the same time. The sequence of operationsdescribed herein can be interrupted, suspended, or otherwise controlledby another process, such as an operating system, kernel, etc. Theroutines can operate in an operating system environment or asstand-alone routines. Functions, routines, methods, steps and operationsdescribed herein can be performed in hardware, software, firmware or anycombination thereof.

Embodiments described herein may be implemented in the form of controllogic in software or hardware or a combination of both. Control logicmay be stored in an information storage medium, such as acomputer-readable medium, as a plurality of instructions adapted todirect an information-processing device to perform a set of stepsdisclosed in the various embodiments. Based on the disclosure andteachings provided herein, a person of ordinary skill in the art willappreciate other ways and/or methods to implement various embodimentsdisclosed herein.

It is also within the spirit and scope of the invention to implement, insoftware programming or code, any of the steps, operations, methods,routines or portions thereof described herein, where such softwareprogramming or code can be stored in a computer-readable medium and canbe operated on by a processor to permit a computer to perform any of thesteps, operations, methods, routines or portions thereof describedherein. The invention may be implemented using software programming orcode in one or more digital computers, by using application specificintegrated circuits, programmable logic devices, field programmable gatearrays; optical, chemical, biological, quantum or nanoengineeredsystems, components and mechanisms may be alternatively, conjunctivelyor sequentially used. In general, various functions of disclosedrepresentative embodiments can be achieved by any means now known orhereafter derived in the art; for example, distributed or networkedsystems, components and circuits may be used. In another example,communication or transfer (or otherwise moving from one place toanother) of data may be wired, wireless, or by any other means.

A “computer-readable medium” may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, system ordevice. The computer readable medium can be, by way of example, but notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, system, device, propagation medium,or computer memory. Such computer-readable media are generallymachine-readable and include software programming or code that can behuman readable (e.g., source code) or machine readable (e.g., objectcode). Examples of non-transitory computer-readable media may includerandom access memories, read-only memories, hard drives, datacartridges, magnetic tapes, floppy diskettes, flash memory drives,optical data storage devices, compact-disc read-only memories, and otherappropriate computer memories and data storage devices. In anillustrative embodiment, some or all of the software components mayreside on a single server computer or on any combination of separateserver computers. As one skilled in the art will appreciate, a computerprogram product implementing various embodiments disclosed herein maycomprise one or more non-transitory computer readable media storingcomputer instructions translatable by one or more processors in acomputing environment.

A “processor” includes any hardware system, mechanism or component thatprocesses data, signals or other information. A processor may include asystem with a central processing unit, an application-specificprocessing unit, multiple processing units, dedicated circuitry forachieving functionality, or other systems. Processing need not belimited to a geographic location, or have temporal limitations. Forexample, a processor can perform its functions in “real-time,”“offline,” in a “batch mode,” etc. Portions of processing may beperformed at different times and at different locations, by different(or the same) processing systems.

It will also be appreciated that one or more of the elements depicted inthe drawings/figures can also be implemented in a more separated orintegrated manner, or even removed or rendered as inoperable in certaincases, as is useful in accordance with a particular application.Additionally, any signal arrows in the drawings/figures should beconsidered only as exemplary, and not limiting, unless otherwisespecifically noted.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having,” or any other contextual variant thereof,are intended to cover a non-exclusive inclusion. For example, a process,product, article, or apparatus that comprises a list of elements is notnecessarily limited only those elements but may include other elementsnot expressly listed or inherent to such process, product, article, orapparatus.

Furthermore, the term “or” as used herein is generally intended to mean“and/or” unless otherwise indicated. For example, a condition A or B issatisfied by any one of the following: A is true (or present) and B isfalse (or not present), A is false (or not present) and B is true (orpresent), and both A and B are true (or present). As used herein, a termpreceded by “a” or “an” (and “the” when antecedent basis is “a” or “an”)includes both singular and plural of such term, unless clearly indicatedotherwise (i.e., that the reference “a” or “an” clearly indicates onlythe singular or only the plural). Also, as used in the descriptionherein, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise. The scope of the disclosure should bedetermined by the following claims and their legal equivalents.

What is claimed is:
 1. A method for auction pricing optimization over anetwork, comprising: receiving, by a data platform from a client device,an asset pricing request containing information about an asset, the dataplatform operating on at least one server machine and supporting anetwork site, the client device communicatively connected to the dataplatform over the network; determining, by an optimization enginerunning on the data platform, a predicted price for the asset at each ofa plurality of locations at each of a plurality of times; computing, bythe optimization engine, a price matrix containing a plurality of valuesfor the predicted price, each value of the plurality of valuesassociated with a specific location of the plurality of locations at aspecific time of the plurality of times; identifying, by theoptimization engine from among the plurality of values at the pluralityof locations relative to the plurality of times in the price matrix, anoptimized predicted price for the asset by: for a given location of theplurality of locations, comparing values associated with different timesof the plurality of times; for a given time of the plurality of times,comparing values associated with different locations of the plurality oflocations; and for a given location of the plurality of locations at agiven time of the plurality of times, comparing values associated withdifferent locations of the plurality of locations at different times ofthe plurality of times; generating a view for presentation of theoptimized predicted price for the asset on the client device; andcommunicating the view to the client device over the network.
 2. Themethod according to claim 1, wherein the determining comprises:determining a predicted price for the asset at a given location at eachof the plurality of times; and determining a predicted price for theasset at different locations of the plurality of locations other thanthe given location at each of the plurality of times.
 3. The methodaccording to claim 1, wherein the determining comprises: computing alinear regression model that defines a set of input variables withassociated regression coefficients, the set of input variablescomprising a first input variable representing supply of the asset at agiven location of the plurality of locations, a second input variablerepresenting supply of the asset at different locations of the pluralityof locations other than the given location, and a third input variablerepresenting supply of competitive assets at the given location.
 4. Themethod according to claim 1, wherein the asset is a vehicle.
 5. Themethod according to claim 4, wherein the determining comprises:computing a linear regression model that defines a set of inputvariables with associated regression coefficients, the set of inputvariables comprising input variables representing attributes describingthe vehicle.
 6. The method according to claim 1, wherein the assetpricing request is for pricing a plurality of assets, wherein theinformation contained in the asset pricing request further includesinformation about each of the plurality of assets, wherein thedetermining, the computing, and the identifying are performed for eachof the plurality of assets, and wherein the view comprises an optimizedpredicted price for each of the plurality of assets.
 7. The methodaccording to claim 1, wherein the asset comprises a set of vehicles,wherein the information contained in the asset pricing request furtherincludes information about each of the set of vehicles, wherein thedetermining comprises performing a valuation of the set of vehicles withrespect to the plurality of locations and the plurality of times.
 8. Asystem for auction pricing optimization over a network, comprising: adata platform operating on at least one server machine and supporting anetwork site, each of the at least one server machine comprising atleast one processor and at least one non-transitory computer readablemedium storing instructions translatable by the at least one processorto perform: receiving, from a client device, an asset pricing requestcontaining information about an asset, the client device communicativelyconnected to the data platform over the network; determining a predictedprice for the asset at each of a plurality of locations at each of aplurality of times; computing a price matrix containing a plurality ofvalues for the predicted price, each value of the plurality of valuesassociated with a specific location of the plurality of locations at aspecific time of the plurality of times; identifying, from among theplurality of values at the plurality of locations relative to theplurality of times in the price matrix, an optimized predicted price forthe asset by: for a given location of the plurality of locations,comparing values associated with different times of the plurality oftimes; for a given time of the plurality of times, comparing valuesassociated with different locations of the plurality of locations; andfor a given location of the plurality of locations at a given time ofthe plurality of times, comparing values associated with differentlocations of the plurality of locations at different times of theplurality of times; generating a view for presentation of the optimizedpredicted price for the asset on the client device; and communicatingthe view to the client device over the network.
 9. The system of claim8, wherein the determining comprises: determining a predicted price forthe asset at a given location at each of the plurality of times; anddetermining a predicted price for the asset at different locations ofthe plurality of locations other than the given location at each of theplurality of times.
 10. The system of claim 8, wherein the determiningcomprises: computing a linear regression model that defines a set ofinput variables with associated regression coefficients, the set ofinput variables comprising a first input variable representing supply ofthe asset at a given location of the plurality of locations, a secondinput variable representing supply of the asset at different locationsof the plurality of locations other than the given location, and a thirdinput variable representing supply of competitive assets at the givenlocation.
 11. The system of claim 8, wherein the asset is a vehicle. 12.The system of claim 11, wherein the determining comprises: computing alinear regression model that defines a set of input variables withassociated regression coefficients, the set of input variablescomprising input variables representing attributes describing thevehicle.
 13. The system of claim 8, wherein the asset pricing request isfor pricing a plurality of assets, wherein the information contained inthe asset pricing request further includes information about each of theplurality of assets, wherein the determining, the computing, and theidentifying are performed for each of the plurality of assets, andwherein the view comprises an optimized predicted price for each of theplurality of assets.
 14. The system of claim 8, wherein the assetcomprises a set of vehicles, wherein the information contained in theasset pricing request further includes information about each of the setof vehicles, wherein the determining comprises performing a valuation ofthe set of vehicles with respect to the plurality of locations and theplurality of times.
 15. A computer program product for auction pricingoptimization over a network, the computer program product comprising atleast one non-transitory computer readable medium storing instructionstranslatable by the at least one processor to perform: receiving, from aclient device, an asset pricing request containing information about anasset, the client device communicatively connected to a data platformover the network, the data platform operating on at least one servermachine and supporting a network site, the at least one server machinecomprising the at least one processor and the at least onenon-transitory computer readable medium; determining a predicted pricefor the asset at each of a plurality of locations at each of a pluralityof times; computing a price matrix containing a plurality of values forthe predicted price, each value of the plurality of values associatedwith a specific location of the plurality of locations at a specifictime of the plurality of times; identifying, from among the plurality ofvalues at the plurality of locations relative to the plurality of timesin the price matrix, an optimized predicted price for the asset by: fora given location of the plurality of locations, comparing valuesassociated with different times of the plurality of times; for a giventime of the plurality of times, comparing values associated withdifferent locations of the plurality of locations; and for a givenlocation of the plurality of locations at a given time of the pluralityof times, comparing values associated with different locations of theplurality of locations at different times of the plurality of times;generating a view for presentation of the optimized predicted price forthe asset on the client device; and communicating the view to the clientdevice over the network.
 16. The computer program product of claim 15,wherein the determining comprises: determining a predicted price for theasset at a given location at each of the plurality of times; anddetermining a predicted price for the asset at different locations ofthe plurality of locations other than the given location at each of theplurality of times.
 17. The computer program product of claim 15,wherein the determining comprises: computing a linear regression modelthat defines a set of input variables with associated regressioncoefficients, the set of input variables comprising a first inputvariable representing supply of the asset at a given location of theplurality of locations, a second input variable representing supply ofthe asset at different locations of the plurality of locations otherthan the given location, and a third input variable representing supplyof competitive assets at the given location.
 18. The computer programproduct of claim 15, wherein the asset is a vehicle and wherein thedetermining comprises: computing a linear regression model that definesa set of input variables with associated regression coefficients, theset of input variables comprising input variables representingattributes describing the vehicle.
 19. The computer program product ofclaim 15, wherein the asset pricing request is for pricing a pluralityof assets, wherein the information contained in the asset pricingrequest further includes information about each of the plurality ofassets, wherein the determining, the computing, and the identifying areperformed for each of the plurality of assets, and wherein the viewcomprises an optimized predicted price for each of the plurality ofassets.
 20. The computer program product of claim 15, wherein the assetcomprises a set of vehicles, wherein the information contained in theasset pricing request further includes information about each of the setof vehicles, wherein the determining comprises performing a valuation ofthe set of vehicles with respect to the plurality of locations and theplurality of times.